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RAG Systems &
Knowledge Agents

Enterprise RAG pipelines that let AI agents securely retrieve, reason over, and act on your internal knowledge — documents, databases, APIs — in real time.

Book a Free Discovery Call → Read the RAG Guide

What Is a RAG System?

RAG (Retrieval-Augmented Generation) is a technique that gives AI models access to your private knowledge base — not just their training data. When a user asks a question, the system retrieves the most relevant documents from your vector database, then generates a grounded, accurate answer.

The result: AI that actually knows your business. Your products, your processes, your contracts, your 10 years of internal research — all queryable in seconds.

Bytolix builds enterprise RAG systems using LlamaIndex, Pinecone, Azure AI Search, and pgvector — with hybrid search, reranking, and citation tracing built in.

Hybrid search — semantic + keyword search for maximum recall
Reranking — cross-encoder reranking for precision at the top
Citation tracing — every answer links back to the source document
Access control — role-based retrieval so agents only see what users should
Evaluation harness — RAGAS scoring to measure faithfulness and relevance

RAG Deployments in Production

Enterprise Knowledge

Enterprise Knowledge Base

10 years of internal research made instantly queryable. What took 3 days now takes 3 minutes. Full audit trail for compliance.

97× faster
Legal AI

Contract Intelligence

Legal agents that ingest contracts, chunk by clause, embed with context, and answer complex legal questions in seconds.

4 hrs → 8 min
Customer Support

Product Support Agent

RAG-powered support agent trained on product documentation, past tickets, and runbooks. Resolves 70% of tickets without human escalation.

70% containment

Let your data
work for you

Your enterprise knowledge is your most valuable asset. A RAG system makes it instantly accessible to AI agents and your entire team.

Book a Free Discovery Call →